Namespace JXG.Math.Statistics
↳ JXG.Math.Statistics
Defined in: statistics.js.
Constructor Attributes | Constructor Name and Description |
---|---|
Functions for mathematical statistics.
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Method Attributes | Method Name and Description |
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<static> |
JXG.Math.Statistics.abs(arr)
Determines the absolute value of every given value.
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<static> |
JXG.Math.Statistics.add(arr1, arr2)
Adds up two (sequences of) values.
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<static> |
JXG.Math.Statistics.div(arr1, arr2)
Divides two (sequences of) values.
|
<static> <deprecated> |
JXG.Math.Statistics.divide()
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<static> |
JXG.Math.Statistics.generateGaussian(mean, stdDev)
Generate values of a standard normal random variable with the Marsaglia polar method, see
https://en.wikipedia.org/wiki/Marsaglia_polar_method .
|
<static> |
JXG.Math.Statistics.max(arr)
Extracts the maximum value from the array.
|
<static> |
JXG.Math.Statistics.mean(arr)
Determines the mean value of the values given in an array.
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<static> |
JXG.Math.Statistics.median(arr)
The median of a finite set of values is the value that divides the set
into two equal sized subsets.
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<static> |
JXG.Math.Statistics.min(arr)
Extracts the minimum value from the array.
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<static> |
JXG.Math.Statistics.mod(arr1, arr2, math)
Divides two (sequences of) values and returns the remainder.
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<static> |
JXG.Math.Statistics.multiply(arr1, arr2)
Multiplies two (sequences of) values.
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<static> |
JXG.Math.Statistics.percentile(arr, percentile)
The P-th percentile ( 0 < P ≤ 100 ) of a list of N ordered values (sorted from least to greatest)
is the smallest value in the list such that no more than P percent of the data is strictly less
than the value and at least P percent of the data is less than or equal to that value.
|
<static> |
JXG.Math.Statistics.prod(arr)
Multiplies all elements of the given array.
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<static> |
JXG.Math.Statistics.range(arr)
Determines the lowest and the highest value from the given array.
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<static> |
JXG.Math.Statistics.sd(arr)
Determines the standard deviation which shows how much
variation there is from the average value of a set of numbers.
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<static> |
JXG.Math.Statistics.subtract(arr1, arr2)
Subtracts two (sequences of) values.
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<static> |
JXG.Math.Statistics.sum(arr)
Sums up all elements of the given array.
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<static> |
JXG.Math.Statistics.TheilSenRegression(coords)
The Theil-Sen estimator can be used to determine a more robust linear regression of a set of sample
points than least squares regression in JXG.Math.Numerics.regressionPolynomial.
|
<static> |
JXG.Math.Statistics.variance(arr)
Bias-corrected sample variance.
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<static> |
JXG.Math.Statistics.weightedMean(arr, w)
Weighted mean value is basically the same as JXG.Math.Statistics.mean but here the values
are weighted, i.e.
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Namespace Detail
JXG.Math.Statistics
Functions for mathematical statistics. Most functions are like in the statistics package R.
Method Detail
<static>
{Array|Number}
JXG.Math.Statistics.abs(arr)
Determines the absolute value of every given value.
- Parameters:
- {Array|Number} arr
- Returns:
- {Array|Number}
<static>
{Array|Number}
JXG.Math.Statistics.add(arr1, arr2)
Adds up two (sequences of) values. If one value is an array and the other one is a number the number
is added to every element of the array. If two arrays are given and the lengths don't match the shortest
length is taken.
- Parameters:
- {Array|Number} arr1
- {Array|Number} arr2
- Returns:
- {Array|Number}
<static>
{Array|Number}
JXG.Math.Statistics.div(arr1, arr2)
Divides two (sequences of) values. If two arrays are given and the lengths don't match the shortest length
is taken.
- Parameters:
- {Array|Number} arr1
- Dividend
- {Array|Number} arr2
- Divisor
- Returns:
- {Array|Number}
<static>
JXG.Math.Statistics.divide()
- Deprecated:
- Use JXG.Math.Statistics.div instead.
<static>
{Number}
JXG.Math.Statistics.generateGaussian(mean, stdDev)
Generate values of a standard normal random variable with the Marsaglia polar method, see
https://en.wikipedia.org/wiki/Marsaglia_polar_method .
- Parameters:
- {Number} mean
- mean value of the normal distribution
- {Number} stdDev
- standard deviation of the normal distribution
- Returns:
- {Number} value of a standard normal random variable
<static>
{Number}
JXG.Math.Statistics.max(arr)
Extracts the maximum value from the array.
- Parameters:
- {Array} arr
- Returns:
- {Number} The highest number from the array. It returns NaN if not every element could be interpreted as a number and -Infinity if an empty array is given or no element could be interpreted as a number.
<static>
{Number}
JXG.Math.Statistics.mean(arr)
Determines the mean value of the values given in an array.
- Parameters:
- {Array} arr
- Returns:
- {Number}
<static>
{Number}
JXG.Math.Statistics.median(arr)
The median of a finite set of values is the value that divides the set
into two equal sized subsets.
- Parameters:
- {Array} arr
- The set of values.
- Returns:
- {Number}
<static>
{Number}
JXG.Math.Statistics.min(arr)
Extracts the minimum value from the array.
- Parameters:
- {Array} arr
- Returns:
- {Number} The lowest number from the array. It returns NaN if not every element could be interpreted as a number and Infinity if an empty array is given or no element could be interpreted as a number.
<static>
{Array|Number}
JXG.Math.Statistics.mod(arr1, arr2, math)
Divides two (sequences of) values and returns the remainder. If two arrays are given and the lengths don't
match the shortest length is taken.
- Parameters:
- {Array|Number} arr1
- Dividend
- {Array|Number} arr2
- Divisor
- {Boolean} math Optional, Default: false
- Mathematical mod or symmetric mod? Default is symmetric, the JavaScript % operator.
- Returns:
- {Array|Number}
<static>
{Array|Number}
JXG.Math.Statistics.multiply(arr1, arr2)
Multiplies two (sequences of) values. If one value is an array and the other one is a number the number
is multiplied to every element of the array. If two arrays are given and the lengths don't match the shortest
length is taken.
- Parameters:
- {Array|Number} arr1
- {Array|Number} arr2
- Returns:
- {Array|Number}
<static>
{Number|Array}
JXG.Math.Statistics.percentile(arr, percentile)
The P-th percentile ( 0 < P ≤ 100 ) of a list of N ordered values (sorted from least to greatest)
is the smallest value in the list such that no more than P percent of the data is strictly less
than the value and at least P percent of the data is less than or equal to that value. See https://en.wikipedia.org/wiki/Percentile.
Here, the linear interpolation between closest ranks method is used.
- Parameters:
- {Array} arr
- The set of values, need not be ordered.
- {Number|Array} percentile
- One or several percentiles
- Returns:
- {Number|Array} Depending if a number or an array is the input for percentile, a number or an array containing the percentils is returned.
<static>
{Number}
JXG.Math.Statistics.prod(arr)
Multiplies all elements of the given array.
- Parameters:
- {Array} arr
- An array of numbers.
- Returns:
- {Number}
<static>
{Array}
JXG.Math.Statistics.range(arr)
Determines the lowest and the highest value from the given array.
- Parameters:
- {Array} arr
- Returns:
- {Array} The minimum value as the first and the maximum value as the second value.
<static>
{Number}
JXG.Math.Statistics.sd(arr)
Determines the standard deviation which shows how much
variation there is from the average value of a set of numbers.
- Parameters:
- {Array} arr
- Returns:
- {Number}
<static>
{Array|Number}
JXG.Math.Statistics.subtract(arr1, arr2)
Subtracts two (sequences of) values. If two arrays are given and the lengths don't match the shortest
length is taken.
- Parameters:
- {Array|Number} arr1
- Minuend
- {Array|Number} arr2
- Subtrahend
- Returns:
- {Array|Number}
<static>
{Number}
JXG.Math.Statistics.sum(arr)
Sums up all elements of the given array.
- Parameters:
- {Array} arr
- An array of numbers.
- Returns:
- {Number}
<static>
{Array}
JXG.Math.Statistics.TheilSenRegression(coords)
The Theil-Sen estimator can be used to determine a more robust linear regression of a set of sample
points than least squares regression in JXG.Math.Numerics.regressionPolynomial.
If the function should be applied to an array a of points, a the coords array can be generated with
JavaScript array.map:
JXG.Math.Statistics.TheilSenRegression(a.map(el => el.coords));
- Parameters:
- {Array} coords
- Array of JXG.Coords.
- Returns:
- {Array} A stdform array of the regression line.
- Examples:
var board = JXG.JSXGraph.initBoard('jxgbox', { boundingbox: [-6,6,6,-6], axis : true }); var a=[]; a[0]=board.create('point', [0,0]); a[1]=board.create('point', [3,0]); a[2]=board.create('point', [0,3]); board.create('line', [ () => JXG.Math.Statistics.TheilSenRegression(a.map(el => el.coords)) ], {strokeWidth:1, strokeColor:'black'});
<static>
{Number}
JXG.Math.Statistics.variance(arr)
Bias-corrected sample variance. A variance is a measure of how far a
set of numbers are spread out from each other.
- Parameters:
- {Array} arr
- Returns:
- {Number}
<static>
{Number}
JXG.Math.Statistics.weightedMean(arr, w)
Weighted mean value is basically the same as JXG.Math.Statistics.mean but here the values
are weighted, i.e. multiplied with another value called weight. The weight values are given
as a second array with the same length as the value array..
- Parameters:
- {Array} arr
- Set of alues.
- {Array} w
- Weight values.
- Throws:
- {Error}
- If the dimensions of the arrays don't match.
- Returns:
- {Number}